Improvements In Processing Research Articles

481-P: 10kHz SCS Restores Mechanical and Thermal Neural Processing in a Rodent Model of Painful Diabetic Neuropathy

A recent randomized controlled trial of 10 kHz spinal cord stimulation (SCS) in patients with painful diabetic neuropathy (PDN) has demonstrated significant and durable improvements in neurologic status, particularly sensory restoration of the foot (Petersen et al 2021, Argoff et al 2023). In order to gain further mechanistic insight into the improvements in sensory processing by 10kHz SCS, we analyzed the receptive field (RF) size and responses of spinal dorsal horn (DH) neurons to mechanical and thermal stimuli of the paw in a PDN rat model. For PDN-model, Rats were given a single effective dose of streptozotocin (STZ), while Naïve animals were injected with 0.9% saline. Four groups were studied: Naïve, STZ, STZ+ShamSCS, and STZ+10kHzSCS. Rats were anesthetized with urethane and a laminectomy was performed for DH neurons recording. We analyzed RF size and the response of DH neurons to vibrotactile stimulation and thermal sensation. Both the Naïve and STZ+10kHzSCS groups had significantly smaller RFs than the STZ Control and STZ+ShamSCS groups. In response to a 1 Hz vibration frequency, Naïve and STZ+10kHzSCS groups had higher peak power spectral density (PSD) of DH firing than STZ+ShamSCS, and PSD for the STZ+10kHzSCS group was statistically higher than for the STZ Control group. Response of DH neurons showed no significant differences between groups during the thermal stimulation, but the post-stimulus firing rates for the STZ+10kHzSCS group were significantly higher compared to STZ Control and STZ+ShamSCS. These data suggest that 10 kHz SCS amplified the DH response to large fiber (vibration) and small fiber (warm) signals and normalized the receptive field of the paw (acuity) in PDN rats. In diabetes, where peripheral sensory compromise from chronic hyperglycemia can lead to loss of protective sensation, such ‘tuned amplification’ as provided by 10 kHz SCS in the central nervous system may compensate for the reduced functionality seen in the peripheral sensory systems. Disclosure K.Lee: Employee; Nevro Corp. D.Lee: Employee; Nevro Corp. D.Wang: Employee; Nevro Corp. Z.B.Kagan: Employee; Nevro Corp. K.Bradley: Employee; Nevro Corp.

Brain Computer Interfaces: The Future of Communication Between the Brain and the External World

This paper provides a comprehensive review of the current state of research on Brain Computer Interfaces (BCIs) and their potential applications. The objective of this study was to gather information from various sources, including journal articles, conference papers, and books, to analyze the advancements and limitations of BCIs. A systematic literature review was conducted using databases such as PubMed, IEEE Xplore, and Google Scholar, with specific keywords related to BCIs and their applications. The selected studies were thoroughly analyzed to identify common themes, methodologies, and key findings. The main contributions of this review include an overview of different types of BCIs, their applications in fields such as medicine, entertainment, and education, and the challenges and limitations faced by BCI technology. The findings highlight the potential of BCIs in restoring motor function, improving the quality of life for individuals with various conditions, and enhancing human-technology interaction. Furthermore, the review identifies future research directions, including signal processing improvements, exploring hybrid and multimodal approaches, conducting long-term real-world studies, addressing ethical considerations, and prioritizing user-centred design. This comprehensive analysis of existing literature provides valuable insights for researchers and practitioners in the field of BCIs and sets the stage for future advancements in this rapidly evolving domain.

Robust Multi-task Learning-based Korean POS Tagging to Overcome Word Spacing Errors

End-to-end neural network-based approaches have recently demonstrated significant improvements in natural language processing (NLP). However, in the NLP application such as assistant systems, NLP components are still processed to extract results using a pipeline paradigm. The pipeline-based concept has issues with error propagation. In Korean, morphological analysis and part-of-speech (POS) tagging step, incorrectly analyzing POS tags for a sentence containing spacing errors negatively affects other modules behind the POS module. Hence, we present a multi-task learning-based POS tagging neural model for Korean with word spacing challenges. When we apply this model to the Korean morphological analysis and POS tagging, we get findings that are robust to word spacing errors. We adopt syllable-level input and output formats, as well as a simple structure for ELECTRA and RNN-CRF models for multi-task learning, and we achieve a good performance 98.30 of F1, better than previous studies on the Sejong corpus test set.

Toward a better understanding of coastal salt marsh mapping: A case from China using dual-temporal images

Coastal salt marshes suffering from anthropogenic coastal development and sea level rise have attracted much attention because of their capacity for carbon sequestration and global climate change mitigation. Accurate mapping of coastal salt marshes is always the first step for their protection, management, and restoration. The inherent complexities of vegetation, dynamics of tides, and anthropogenic disturbances pose challenges for remote sensing-based approaches. Existing studies have utilized phenology information and various black-box algorithms to reduce misclassifications. However, the approaches with dual-temporal images containing phenology information have suffered from inefficiency; the misclassifications have been objectively post-processed rather than considered in the method design, and the tacit knowledge of the trained black-box models has not been revealed. To address the above issues, we proposed a new approach with solid improvements in dual-temporal image construction, misclassification processing, and tacit knowledge analysis, including an efficient method to synthesize dual-temporal images based on the common divisor of the green-up season or senescence season resulting from latitudinal gradients in narrow coastal areas of China, a detailed classification scheme by interpretation of iteratively collected samples, and a key decision rule approximating the trained model. We applied the approach to Sentinel-1/2 images and DEM data, thus deriving a 10-m resolution coastal salt marsh map of China with an overall accuracy of 92.5%. A qualitative comparison showed that the map produced in this study was fitted well with actual salt marsh distributions, resulting in improved accuracy when compared to recently generated maps. The most important contribution is that the overall nature of the trained model observed from the training samples was approximated by a four-feature decision rule following the principle of explainable artificial intelligence, i.e., B8senescence/B4senescence < 2.06 & B4green/B8green < 0.78 & B12green/B11green < 0.72 & elevation < 2.13, and thus established a new potential classification mechanism for dual-temporal black-box algorithms, i.e., the water signal was covered up by flourishing vegetation, but the situation changed when vegetation withered. This study not only generates an accurate coastal salt marsh map at a national scale but also provides a classification mechanism for dual-temporal image-based coastal salt marsh identification and mapping.

Inter-rater agreement for the annotation of neurologic signs and symptoms in electronic health records.

The extraction of patient signs and symptoms recorded as free text in electronic health records is critical for precision medicine. Once extracted, signs and symptoms can be made computable by mapping to signs and symptoms in an ontology. Extracting signs and symptoms from free text is tedious and time-consuming. Prior studies have suggested that inter-rater agreement for clinical concept extraction is low. We have examined inter-rater agreement for annotating neurologic concepts in clinical notes from electronic health records. After training on the annotation process, the annotation tool, and the supporting neuro-ontology, three raters annotated 15 clinical notes in three rounds. Inter-rater agreement between the three annotators was high for text span and category label. A machine annotator based on a convolutional neural network had a high level of agreement with the human annotators but one that was lower than human inter-rater agreement. We conclude that high levels of agreement between human annotators are possible with appropriate training and annotation tools. Furthermore, more training examples combined with improvements in neural networks and natural language processing should make machine annotators capable of high throughput automated clinical concept extraction with high levels of agreement with human annotators.

Real world evidence of improved attention and cognition during physical therapy paired with neuromodulation: a brain vital signs study.

Non-invasive neuromodulation using translingual neurostimulation (TLNS) has been shown to advance rehabilitation outcomes, particularly when paired with physical therapy (PT). Together with motor gains, patient-reported observations of incidental improvements in cognitive function have been noted. Both studies in healthy individuals and case reports in clinical populations have linked TLNS to improvements in attention-related cognitive processes. We investigated if the use of combined TLNS/PT would translate to changes in objective neurophysiological cognitive measures in a real-world clinical sample of patients from two separate rehabilitation clinics. Brain vital signs were derived from event-related potentials (ERPs), specifically auditory sensation (N100), basic attention (P300), and cognitive processing (N400). Additional analyses explored the attention-related N200 response given prior evidence of attention effects from TLNS/PT. The real-world patient sample included a diverse clinical group spanning from mild-to-moderate traumatic brain injury (TBI), stroke, Multiple Sclerosis (MS), Parkinson's Disease (PD), and other neurological conditions. Patient data were also acquired from a standard clinical measure of cognition for comparison. Results showed significant N100 variation between baseline and endpoint following TLNS/PT treatment, with further examination showing condition-specific significant improvements in attention processing (i.e., N100 and N200). Additionally, CogBAT composite scores increased significantly from baseline to endpoint. The current study highlighted real-world neuromodulation improvements in neurophysiological correlates of attention. Overall, the real-world findings support the concept of neuromodulation-related improvements extending beyond physical therapy to include potential attention benefits for cognitive rehabilitation.

Transfer Learning in Natural Language Processing (NLP)

Purpose: The purpose of this study is to address the limited use of transfer learning techniques in radio frequency machine learning and to propose a customized taxonomy for radio frequency applications. The aim is to enable performance gains, improved generalization, and cost-effective training data solutions in this specific domain.&#x0D; Methodology: The research design employed in this study involves a comprehensive review of existing literature on transfer learning in radio frequency machine learning. The researchers collected relevant papers from reputable sources and analyzed them to identify patterns, trends, and insights. The method of data collection primarily relied on examining and synthesizing existing literature. Data analysis involved identifying key findings and developing a customized taxonomy for radio frequency applications.&#x0D; Findings: The study's findings highlight the limited utilization of transfer learning techniques in radio frequency machine learning. While transfer learning has shown significant performance improvements in computer vision and natural language processing, its potential in the wireless communications domain has yet to be fully explored. The customized taxonomy proposed in this study provides a consistent framework for analyzing and comparing existing and future efforts in this field.&#x0D; Recommendations: Based on the findings, the study recommends further research and experimentation to explore the potential of transfer learning techniques in radio frequency machine learning. This includes investigating performance gains, improving generalization capabilities, and addressing concerns related to training data costs. Additionally, collaborations between researchers and practitioners in the field are encouraged to facilitate knowledge exchange and foster innovation. Practice: To practitioners in the field of radio frequency machine learning, this study emphasizes the potential benefits of incorporating transfer learning techniques. It encourages practitioners to explore the application of transfer learning in their specific domain, leveraging prior knowledge to enhance performance and address training data challenges. It also highlights the importance of staying informed about the latest developments and collaborating with experts in the field. Policy: To policy makers, the study underscores the need for supportive policies that promote research and development in radio frequency machine learning. It recommends creating an environment that fosters innovation, encourages collaborations between academia and industry, and provides resources and incentives for further exploration of transfer learning techniques. Policy makers should consider the potential impact of transfer learning on the wireless communications industry and support initiatives that enhance its adoption and implementation.&#x0D;

Effects of integrated hospital treatment on the default mode, salience, and frontal-parietal networks in anorexia nervosa: A longitudinal resting-state functional magnetic resonance imaging study.

The psychopathology of patients with anorexia nervosa has been hypothesized to involve inappropriate self-referential processing, disturbed interoceptive awareness, and excessive cognitive control, including distorted self-concern, disregard of their own starvation state, and extreme weight-control behavior. We hypothesized that the resting-state brain networks, including the default mode, salience and frontal-parietal networks, might be altered in such patients, and that treatment might normalize neural functional connectivity, with improvement of inappropriate self-cognition. We measured resting-state functional magnetic resonance images from 18 patients with anorexia nervosa and 18 healthy subjects before and after integrated hospital treatment (nourishment and psychological therapy). The default mode, salience, and frontal-parietal networks were examined using independent component analysis. Body mass index and psychometric measurements significantly improved after treatment. Before treatment, default mode network functional connectivity in the retrosplenial cortex and salience network functional connectivity in the ventral anterior insula and rostral anterior cingulate cortex were decreased in anorexia nervosa patients compared with those in controls. Interpersonal distrust was negatively correlated with salience network functional connectivity in the rostral anterior cingulate cortex. Default mode network functional connectivity in the posterior insula and frontal-parietal network functional connectivity in the angular gyrus were increased in anorexia nervosa patients compared with those in controls. Comparison between pre- and post-treatment images from patients with anorexia nervosa exhibited significant increases in default mode network functional connectivity in the hippocampus and retrosplenial cortex, and salience network functional connectivity in the dorsal anterior insula following treatment. Frontal-parietal network functional connectivity in the angular cortex showed no significant changes. The findings revealed that treatment altered the functional connectivity in several parts of default mode and salience networks in patients with anorexia nervosa. These alterations of neural function might be associated with improvement of self-referential processing and coping with sensations of discomfort following treatment for anorexia nervosa.

Insight into the ionic liquid tetraphenylphosphine salt on the processability improvement for PEEK

AbstractIn this article, a high‐temperature resistant ionic liquid ([(Ph)4P]+[N(CF3SO3)2]−) was introduced into PEEK to improve its processing performance. The effect of the IL on the rheology, crystalline, thermal, and mechanical properties of PEEK was analyzed. The rheological results show that the viscosity of PEEK decreases after adding IL, and the complex viscosity of PEEK decreases to 490 Pa s after adding 5 wt% IL, which is 15% lower than that of neat PEEK. Besides, it was found that adding a small amount of IL improved the crystallization ability of PEEK, shortened the crystallization time, and increased the tensile strength of PEEK blends. Additionally, IL has no significant effect on the molecular weight and degradation temperature of PEEK. In summary, adding IL into the PEEK matrix reduces the melt viscosity of PEEK, providing a reference for improving the processing fluidity of PEEK.

Toward Nonepitaxial Laser Diodes.

Thin-film organic, colloidal quantum dot, and metal halide perovskite semiconductors are all being pursued in the quest for a wavelength-tunable diode laser technology that does not require epitaxial growth on a traditional semiconductor substrate. Despite promising demonstrations of efficient light-emitting diodes and low-threshold optically pumped lasing in each case, there are still fundamental and practical barriers that must be overcome to reliably achieve injection lasing. This review outlines the historical development and recent advances of each material system on the path to a diode laser. Common challenges in resonator design, electrical injection, and heat dissipation are highlighted, as well as the different optical gain physics that make each system unique. The evidence to date suggests that continued progress for organic and colloidal quantum dot laser diodes will likely hinge on the development of new materials or indirect pumping schemes, while improvements in device architecture and film processing are most critical for perovskite lasers. In all cases, systematic progress will require methods that can quantify how close new devices get with respect to their electrical lasing thresholds. We conclude by discussing the current status of nonepitaxial laser diodes in the historical context of their epitaxial counterparts, which suggests that there is reason to be optimistic for the future.

Reactive end-group polybenzimidazoles for high temperature thermosets

Polybenzimidazoles (PBI) are heterocyclic polymers that possess high thermal and oxidative stability. The inherent strength and chemical and thermal stability of PBI make it ideal for use in extreme environments, however, the current processing of PBI molded parts makes the process practically and economically unfavorable. This work explores the improvement of PBI processability using a reactive oligomer approach. An easily processable PBI resin was synthesized, characterized, and tested in molding studies. The resin consists of PBI oligomers containing the p,p-OPBI chemistry (-O- linkage) with reactive end groups that crosslink at high temperature. The use of oligomers decreases the melt processing temperature of PBI, allowing for processability in less extreme conditions. The addition of reactive end groups allows for crosslinking during a post molding thermal cure, which imparts the high mechanical properties associated with PBI. The p,p-OPBI molded and cured articles exhibited tensile strengths of greater than 60 MPa, decreased water uptake and possessed high chemical and thermal stability.

A Research Perspective on Data Management Techniques for Federated Cloud Environment

Cloud computing has given a large scope of improvement in processing, storage and retrieval of data that is generated in huge amount from devices and users. Heterogenous devices and users generates the multidisciplinary data that needs to take care for easy and efficient storage and fast retrieval by maintaining quality and service level agreements. By just storing the data in cloud will not full fill the user requirements, the data management techniques has to be applied so that data adaptiveness and proactiveness characteristics are upheld. To manage the effectiveness of entire eco system a middleware must be there in between users and cloud service providers. Middleware has set of events and trigger based policies that will act on generated data to intermediate users and cloud service providers. For cloud service providers to deliver an efficient utilization of resources is one of the major issues and has scope of improvement in the federation of cloud service providers to fulfill user’s dynamic demands. Along with providing adaptiveness of data management in the middleware layer is challenging. In this paper, the policies of middleware for adaptive data management have been reviewed extensively. The main objectives of middleware are also discussed to accomplish high throughput of cloud service providers by means of federation and qualitative data management by means of adaptiveness and proactiveness. The cloud federation techniques have been studied thoroughly along with the pros and cons of it. Also, the strategies to do management of data has been exponentially explored.

Imaging of Fiber Waviness in Thick Composites with Unknown Material Properties Using Probability-Based Ultrasound Non-Reciprocity.

Fiber waviness and voids may be produced in thick composites due to improper manufacturing conditions and consequently pose a risk of structural failure. A proof-of-concept solution for imaging fiber waviness in thick porous composites was proposed from both numerical and experimental studies, via calculating ultrasound non-reciprocity along different wave paths in a sensing network constructed by two phased array probes. Time-frequency analyses were conducted to reveal the cause of ultrasound non-reciprocity in wavy composites. Subsequently, the number of elements in the probes and excitation voltages was determined for fiber waviness imaging using the ultrasound non-reciprocity with a probability-based diagnostic algorithm. The fiber angle gradient was observed to cause ultrasound non-reciprocity and fiber waviness in the thick wavy composites were successfully imaged regardless of presence of voids. This study proposes a new feature for the ultrasonic imaging of fiber waviness and is expected to contribute to processing improvement in thick composites without prior knowledge of material anisotropy.

Peeking inside the mantle structure beneath the Italian region through SKS shear wave splitting anisotropy: a review

Over the years, seismic anisotropy characterization has become one of the most popular methods to study and understand the Earth’s deep structures. Starting from more than 20 years ago, considerable progress has been made to map the anisotropic structure beneath Italy and the Central Mediterranean area. In particular, several past and current international projects (such as RETREAT, CAT/SCAN, CIFALPS, CIFALPS-2, AlpArray) focused on retrieving the anisotropic structure beneath Italy and surrounding regions, promoting advances in the knowledge of geological and geodynamical setting of this intriguing area. All of these studies aimed at a better understanding the complex and active geodynamic evolution of both the active and remnant subduction systems characterising this region and the associated Apennines, Alps and Dinaric belts, together with the Adriatic and Tyrrhenian basins. The presence of dense high-quality seismic networks, permanently run by INGV and other institutions, and temporary seismic stations deployed in the framework of international projects, the improvements in data processing and the use of several and even more sophisticated methods proposed to quantify the anisotropy, allowed to collect a huge amount of anisotropic parameters. Here a collection of all measurements done on core refracted phases are shown and used as a measure of mantle deformation and interpreted into geodynamic models. Images of anisotropy identify well-developed mantle flows around the sinking European and Adriatic slabs, recognised by tomographic studies. Slab retreat and related mantle flow are interpreted as the main driving mechanism of the Central Mediterranean geodynamics.

From ChatGPT-3 to GPT-4: A Significant Advancement in AI-Driven NLP Tools

Recent improvements in Natural Language Processing (NLP) have led to the creation of powerful language models like Chat Generative Pre-training Transformer (ChatGPT), Google’s BARD, Ernie which has shown to be very good at many different language tasks. But as language tasks get more complicated, having even more advanced NLP tool is essential nowadays. In this study, researchers look at how the latest versions of the GPT language model(GPT-4 &amp; 5) can help with these advancements. The research method for this paper is based on a narrative analysis of the literature, which makes use of secondary data gathered from previously published studies including articles, websites, blogs, and visual and numerical facts etc. Findings of this study revealed that GPT-4 improves the model's training data, the speed with which it can be computed, the flawless answers that it provides with, and its overall performance. This study also shows that GPT-4 does much better than GPT-3.5 at translating languages, answering questions, and figuring out how people feel about things. The study provides a solid basis for building even more advanced NLP tools and programmes like GPT-5. The study will help the AI &amp; LLM researchers, NLP developers and academicians in exploring more into this particular field of study. As this is the first kind of research comparing two NLP tools, therefore researchers suggested going for a quantitative research in the near future to validate the findings of this research.

The Treatment of Endocrine-Disruptive Chemicals in Wastewater through Asymmetric Reverse Osmosis Membranes: A Review

Endocrine-disrupting chemicals (EDCs) present in aquatic environment have been regarded as detrimental organic pollutants that pose significant adverse impacts on human health and the aquatic ecosystem. The removal of EDCs is highly desired to mitigate their harmful effects. Physical treatment through membrane-based separation processes is an attractive approach, as it can effectively remove a wide range of recalcitrant organic and nonorganic EDCs. In particular, the reverse osmosis (RO) process has shown promise in removing EDCs of various concentrations and from different sources. Recently, the development of innovative asymmetric RO membranes has become the forefront in this field. Various membrane modification strategies have been commenced to address the limitations of commercial membranes. This review provides an overview of the recent advances in asymmetric RO membranes for EDC removal from water and wastewater system. The potential areas of improvement for RO processes and RO membranes are also highlighted. Based on the existing literature using RO for EDC removal from water, the most investigated EDCs are bisphenol A (BPA) and caffeine in the concentration range of 200 ppb to 100 ppm. Polyamide RO membranes have been shown to remove EDCs from water bodies with a removal efficiency of ~30 to 99%, largely depending on the type and concentration of the treated EDCs, as well as the properties of the RO membranes. It has been demonstrated that the performance can be further heightened by tailoring the properties of RO membranes and optimizing the operating conditions of the RO process.

Benchmarking and validation of a hybrid model for electropositive and electronegative capacitively coupled plasmas

In this work, a fluid/Monte Carlo collision (fluid/MCC) hybrid model is developed based on the framework of multi-physics analysis of plasma sources. This hybrid model could be highly accurate in predicting the nonequilibrium phenomena in capacitively coupled plasmas and meanwhile avoid the limitation caused by the computational cost. Benchmarking against the well-established particle-in-cell/MCC (PIC/MCC) method and comparison with experimental data have been presented both in electropositive N2 discharges and electronegative O2 discharges. The results indicate that in N2 discharges, the ion density evolves from a uniform distribution to an edge-high profile as power increases. Besides, the electron energy distribution function (EEDF) at the bulk center exhibits a ‘hole’ at about 3 eV, and the ‘hole’ becomes less obvious at the radial edge, because more low energy electrons are generated there. In O2 discharges, the EEDF exhibits a Druyvesteyn-like distribution in the bulk region, and it evolves to a Maxwellian distribution in the sheath, indicating the dominant influence of the electric field heating there. The results obtained by the hybrid model agree well with those calculated by the PIC/MCC method, as well as those measured by double probe, except for a slight discrepancy in absolute values. The qualitative agreement achieved in this work validates the potential of this hybrid model as an effective tool in the deep understanding of plasma properties, as well as in the improvement of plasma processing.

Identification of Key Components Responsible for the Aromatic Quality of Jinmudan Black Tea by Means of Molecular Sensory Science.

A fruity aroma is regarded as an important factor in the evaluation of black tea quality. However, the compounds contributing to a particularly fruity aroma still garner less attention. In this study, we aimed to identify the aroma-active compounds of the peach-like aroma of Jinmudan black tea (JBT). We used gas chromatography-mass spectrometry (GC-MS) to reveal the profile of the chemical compounds integrated into JBT and identified terpenoids, heterocyclic, and esters that contribute to its floral and fruity aroma. Under the PCA and PLS-DA modes, JBT and Fuyun NO. 6 black tea (FBT) can be divided into two classes, respectively (class 1 and class 2); several compounds, including indole, methyl salicylate, and δ-decalactone, have a higher VIP value (Variable Importance in Projection), and it has been found that δ-decalactone was the characteristic aromatic compound of peach fruit. Gas chromatography-olfactometry (GC-O) and the odor activity value (OAV) indicated that, in JBT, linalool, phenylacetaldehyde, and δ-decalactone could be considered aroma-active compounds (AACs). However, in FBT, the high content of heterocyclic compounds contribute to its caramel-like aroma. As for the biochemical compounds measurement, JBT has a higher content of theaflavins (TFs), thearubigins (TRs), and flavonoids. These results provide a theoretical basis for the quality and processing improvement in JBT.

Civilian armored vehicle operations in Brazil – challenges and production processes improvements: a qualitative survey

Abstract Armoring civilian vehicles requires specialized knowledge and experience that many armoring companies lack as they are not direct or indirect suppliers of vehicle manufacturers. This limits their access to automotive quality and manufacturing certifications or detailed vehicle designs, which can result in loss or malfunctioning of automotive components during the armoring process. Therefore, this study aimed to investigate the challenges faced by Brazilian civilian armoring companies and identify opportunities for improvement in their production processes. Qualitative research was conducted using a questionnaire-based survey of eight specialized firms in Brazil, as well as literature related to DFMA, design for manufacturing and assembly, quality, automotive, and ballistic references. The study results include detailed armoring operation steps, qualitative survey reports, and helpful literature references for armoring practitioners to generate a standard armoring procedure for different vehicle models. Following best practices in automotive and armoring procedures collected in the survey responses can standardize and enhance ballistic protection operations while preserving the original vehicle systems' functionalities and warranties. This work provides valuable information for armoring companies to improve their operations and interfaces with automotive systems and follow automotive and ballistic references.

In-situ polycondensate-coated cellulose nanofiber heterostructure for polylactic acid-based composites with superior mechanical and thermal properties

Renewable yet biodegradable natural fiber (e.g., cellulose nanofiber (CNF)) reinforced bio-based polymers (e.g., polylactic acid (PLA)) are being applied for the manufacture of clean packaging products. The interface incompatibility between hydrophilic CNF and hydrophobic PLA still restricts the promotion of high-performance bio-based products. Herein, a polycondensate-coated CNF hybrid, wherein silane, aluminate, and titanate coupling agent monomers were in-situ polymerized onto the CNF surface via dehydration self-condensation, was designed and further employed as strengthening/toughening nanofillers for fabricating the CNF-reinforced PLA composite. Results showed that the polycondensate coatings could efficiently promote the dispersion of CNFs and enhance interfacial compatibility between CNFs and PLA. Attributing to the synergistic effect of polycondensate coatings and CNFs, a considerable improvement in processing, mechanical and thermal properties was obtained in resultant CNF/PLA composites. With adding 2.5 wt% polycondensate-coated CNFs, the tensile strength, Young's modulus, and tensile toughness of CNF-reinforced PLA composites was raised by about 27 %, 51 % and 68 %, respectively; also, such composite possessed greater elasticity and higher melt strength than pure PLA. This study provides a novel interface control strategy to fabricate low-cost yet high-performance PLA-based composites for sustainable packaging application.